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afrivoices-whisper-turbo-50h

Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
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xet
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How to use from the
Use from the
Transformers library
# Gated model: Login with a HF token with gated access permission
hf auth login
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="evie-8/afrivoices-whisper-turbo-50h")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("evie-8/afrivoices-whisper-turbo-50h")
model = AutoModelForSpeechSeq2Seq.from_pretrained("evie-8/afrivoices-whisper-turbo-50h", device_map="auto")
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afrivoices-whisper-turbo-50h

This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6472
  • Wer: 0.2822

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.3245 0.0926 500 1.2512 1.1332
2.0196 0.1852 1000 1.1085 0.9513
1.9094 0.2778 1500 0.9980 0.5464
1.7282 0.3704 2000 0.9755 0.5172
1.5461 0.4630 2500 0.9266 0.5150
1.5012 0.5556 3000 0.9051 0.4016
1.4850 0.6482 3500 0.8571 0.3469
1.5167 0.7408 4000 0.8240 0.3211
1.4645 0.8334 4500 0.8260 0.3582
1.4691 0.9260 5000 0.7812 0.4899
1.2002 1.0185 5500 0.7821 0.5344
1.1558 1.1111 6000 0.7543 0.4379
1.1442 1.2037 6500 0.7418 0.5382
1.2177 1.2963 7000 0.7233 0.5535
1.1813 1.3889 7500 0.7229 0.3162
1.1705 1.4815 8000 0.7128 0.2949
1.1547 1.5741 8500 0.6989 0.3372
1.1229 1.6667 9000 0.6993 0.3215
1.1229 1.7593 9500 0.6784 0.3013
1.0661 1.8519 10000 0.6844 0.2725
1.0986 1.9445 10500 0.6725 0.3608
0.9513 2.0370 11000 0.6840 0.2766
0.8773 2.1296 11500 0.6763 0.2972
0.9462 2.2222 12000 0.6605 0.2766
0.9006 2.3148 12500 0.6592 0.2668
0.9189 2.4074 13000 0.6666 0.2642
0.9381 2.5000 13500 0.6559 0.2818
0.8477 2.5926 14000 0.6580 0.3050
0.8142 2.6852 14500 0.6514 0.3088
0.8875 2.7778 15000 0.6503 0.2762
0.8470 2.8705 15500 0.6446 0.2736
0.7688 2.9631 16000 0.6476 0.2814
0.7720 3.0 16200 0.6472 0.2822

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.11.0+cu128
  • Datasets 5.0.0
  • Tokenizers 0.22.2
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